--- /dev/null
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains the definition for inception v3 classification network."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from nets import inception_utils
+
+slim = tf.contrib.slim
+trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
+
+
+def inception_v3_base(inputs,
+ final_endpoint='Mixed_7c',
+ min_depth=16,
+ depth_multiplier=1.0,
+ scope=None):
+ """Inception model from http://arxiv.org/abs/1512.00567.
+
+ Constructs an Inception v3 network from inputs to the given final endpoint.
+ This method can construct the network up to the final inception block
+ Mixed_7c.
+
+ Note that the names of the layers in the paper do not correspond to the names
+ of the endpoints registered by this function although they build the same
+ network.
+
+ Here is a mapping from the old_names to the new names:
+ Old name | New name
+ =======================================
+ conv0 | Conv2d_1a_3x3
+ conv1 | Conv2d_2a_3x3
+ conv2 | Conv2d_2b_3x3
+ pool1 | MaxPool_3a_3x3
+ conv3 | Conv2d_3b_1x1
+ conv4 | Conv2d_4a_3x3
+ pool2 | MaxPool_5a_3x3
+ mixed_35x35x256a | Mixed_5b
+ mixed_35x35x288a | Mixed_5c
+ mixed_35x35x288b | Mixed_5d
+ mixed_17x17x768a | Mixed_6a
+ mixed_17x17x768b | Mixed_6b
+ mixed_17x17x768c | Mixed_6c
+ mixed_17x17x768d | Mixed_6d
+ mixed_17x17x768e | Mixed_6e
+ mixed_8x8x1280a | Mixed_7a
+ mixed_8x8x2048a | Mixed_7b
+ mixed_8x8x2048b | Mixed_7c
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ final_endpoint: specifies the endpoint to construct the network up to. It
+ can be one of ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
+ 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3',
+ 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c',
+ 'Mixed_6d', 'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'].
+ min_depth: Minimum depth value (number of channels) for all convolution ops.
+ Enforced when depth_multiplier < 1, and not an active constraint when
+ depth_multiplier >= 1.
+ depth_multiplier: Float multiplier for the depth (number of channels)
+ for all convolution ops. The value must be greater than zero. Typical
+ usage will be to set this value in (0, 1) to reduce the number of
+ parameters or computation cost of the model.
+ scope: Optional variable_scope.
+
+ Returns:
+ tensor_out: output tensor corresponding to the final_endpoint.
+ end_points: a set of activations for external use, for example summaries or
+ losses.
+
+ Raises:
+ ValueError: if final_endpoint is not set to one of the predefined values,
+ or depth_multiplier <= 0
+ """
+ # end_points will collect relevant activations for external use, for example
+ # summaries or losses.
+ end_points = {}
+
+ if depth_multiplier <= 0:
+ raise ValueError('depth_multiplier is not greater than zero.')
+ depth = lambda d: max(int(d * depth_multiplier), min_depth)
+
+ with tf.variable_scope(scope, 'InceptionV3', [inputs]):
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+ stride=1, padding='VALID'):
+ # 299 x 299 x 3
+ end_point = 'Conv2d_1a_3x3'
+ net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 149 x 149 x 32
+ end_point = 'Conv2d_2a_3x3'
+ net = slim.conv2d(net, depth(32), [3, 3], scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 147 x 147 x 32
+ end_point = 'Conv2d_2b_3x3'
+ net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 147 x 147 x 64
+ end_point = 'MaxPool_3a_3x3'
+ net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 73 x 73 x 64
+ end_point = 'Conv2d_3b_1x1'
+ net = slim.conv2d(net, depth(80), [1, 1], scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 73 x 73 x 80.
+ end_point = 'Conv2d_4a_3x3'
+ net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 71 x 71 x 192.
+ end_point = 'MaxPool_5a_3x3'
+ net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # 35 x 35 x 192.
+
+ # Inception blocks
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+ stride=1, padding='SAME'):
+ # mixed: 35 x 35 x 256.
+ end_point = 'Mixed_5b'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
+ scope='Conv2d_0b_5x5')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+ scope='Conv2d_0b_3x3')
+ branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+ scope='Conv2d_0c_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_1: 35 x 35 x 288.
+ end_point = 'Mixed_5c'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
+ scope='Conv_1_0c_5x5')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(64), [1, 1],
+ scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+ scope='Conv2d_0b_3x3')
+ branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+ scope='Conv2d_0c_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_2: 35 x 35 x 288.
+ end_point = 'Mixed_5d'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
+ scope='Conv2d_0b_5x5')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+ scope='Conv2d_0b_3x3')
+ branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
+ scope='Conv2d_0c_3x3')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_3: 17 x 17 x 768.
+ end_point = 'Mixed_6a'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
+ scope='Conv2d_0b_3x3')
+ branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_1x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+ scope='MaxPool_1a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed4: 17 x 17 x 768.
+ end_point = 'Mixed_6b'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(128), [1, 7],
+ scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+ scope='Conv2d_0c_7x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
+ scope='Conv2d_0b_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(128), [1, 7],
+ scope='Conv2d_0c_1x7')
+ branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
+ scope='Conv2d_0d_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+ scope='Conv2d_0e_1x7')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_5: 17 x 17 x 768.
+ end_point = 'Mixed_6c'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
+ scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+ scope='Conv2d_0c_7x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+ scope='Conv2d_0b_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
+ scope='Conv2d_0c_1x7')
+ branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+ scope='Conv2d_0d_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+ scope='Conv2d_0e_1x7')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # mixed_6: 17 x 17 x 768.
+ end_point = 'Mixed_6d'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
+ scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+ scope='Conv2d_0c_7x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+ scope='Conv2d_0b_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
+ scope='Conv2d_0c_1x7')
+ branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
+ scope='Conv2d_0d_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+ scope='Conv2d_0e_1x7')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_7: 17 x 17 x 768.
+ end_point = 'Mixed_6e'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
+ scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+ scope='Conv2d_0c_7x1')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
+ scope='Conv2d_0b_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+ scope='Conv2d_0c_1x7')
+ branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
+ scope='Conv2d_0d_7x1')
+ branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
+ scope='Conv2d_0e_1x7')
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
+ scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_8: 8 x 8 x 1280.
+ end_point = 'Mixed_7a'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
+ scope='Conv2d_0b_1x7')
+ branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
+ scope='Conv2d_0c_7x1')
+ branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2,
+ padding='VALID', scope='Conv2d_1a_3x3')
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
+ scope='MaxPool_1a_3x3')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ # mixed_9: 8 x 8 x 2048.
+ end_point = 'Mixed_7b'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
+ slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(
+ branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
+ branch_2 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
+ slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(
+ branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+
+ # mixed_10: 8 x 8 x 2048.
+ end_point = 'Mixed_7c'
+ with tf.variable_scope(end_point):
+ with tf.variable_scope('Branch_0'):
+ branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
+ with tf.variable_scope('Branch_1'):
+ branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
+ branch_1 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
+ slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
+ with tf.variable_scope('Branch_2'):
+ branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
+ branch_2 = slim.conv2d(
+ branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
+ branch_2 = tf.concat(axis=3, values=[
+ slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
+ slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
+ with tf.variable_scope('Branch_3'):
+ branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
+ branch_3 = slim.conv2d(
+ branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
+ net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3])
+ end_points[end_point] = net
+ if end_point == final_endpoint: return net, end_points
+ raise ValueError('Unknown final endpoint %s' % final_endpoint)
+
+
+def inception_v3(inputs,
+ num_classes=1000,
+ is_training=True,
+ dropout_keep_prob=0.8,
+ min_depth=16,
+ depth_multiplier=1.0,
+ prediction_fn=slim.softmax,
+ spatial_squeeze=True,
+ reuse=None,
+ scope='InceptionV3'):
+ """Inception model from http://arxiv.org/abs/1512.00567.
+
+ "Rethinking the Inception Architecture for Computer Vision"
+
+ Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens,
+ Zbigniew Wojna.
+
+ With the default arguments this method constructs the exact model defined in
+ the paper. However, one can experiment with variations of the inception_v3
+ network by changing arguments dropout_keep_prob, min_depth and
+ depth_multiplier.
+
+ The default image size used to train this network is 299x299.
+
+ Args:
+ inputs: a tensor of size [batch_size, height, width, channels].
+ num_classes: number of predicted classes.
+ is_training: whether is training or not.
+ dropout_keep_prob: the percentage of activation values that are retained.
+ min_depth: Minimum depth value (number of channels) for all convolution ops.
+ Enforced when depth_multiplier < 1, and not an active constraint when
+ depth_multiplier >= 1.
+ depth_multiplier: Float multiplier for the depth (number of channels)
+ for all convolution ops. The value must be greater than zero. Typical
+ usage will be to set this value in (0, 1) to reduce the number of
+ parameters or computation cost of the model.
+ prediction_fn: a function to get predictions out of logits.
+ spatial_squeeze: if True, logits is of shape [B, C], if false logits is
+ of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
+ reuse: whether or not the network and its variables should be reused. To be
+ able to reuse 'scope' must be given.
+ scope: Optional variable_scope.
+
+ Returns:
+ logits: the pre-softmax activations, a tensor of size
+ [batch_size, num_classes]
+ end_points: a dictionary from components of the network to the corresponding
+ activation.
+
+ Raises:
+ ValueError: if 'depth_multiplier' is less than or equal to zero.
+ """
+ if depth_multiplier <= 0:
+ raise ValueError('depth_multiplier is not greater than zero.')
+ depth = lambda d: max(int(d * depth_multiplier), min_depth)
+
+ with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes],
+ reuse=reuse) as scope:
+ with slim.arg_scope([slim.batch_norm, slim.dropout],
+ is_training=is_training):
+ net, end_points = inception_v3_base(
+ inputs, scope=scope, min_depth=min_depth,
+ depth_multiplier=depth_multiplier)
+
+ # Auxiliary Head logits
+ with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
+ stride=1, padding='SAME'):
+ aux_logits = end_points['Mixed_6e']
+ with tf.variable_scope('AuxLogits'):
+ aux_logits = slim.avg_pool2d(
+ aux_logits, [5, 5], stride=3, padding='VALID',
+ scope='AvgPool_1a_5x5')
+ aux_logits = slim.conv2d(aux_logits, depth(128), [1, 1],
+ scope='Conv2d_1b_1x1')
+
+ # Shape of feature map before the final layer.
+ kernel_size = _reduced_kernel_size_for_small_input(
+ aux_logits, [5, 5])
+ aux_logits = slim.conv2d(
+ aux_logits, depth(768), kernel_size,
+ weights_initializer=trunc_normal(0.01),
+ padding='VALID', scope='Conv2d_2a_{}x{}'.format(*kernel_size))
+ aux_logits = slim.conv2d(
+ aux_logits, num_classes, [1, 1], activation_fn=None,
+ normalizer_fn=None, weights_initializer=trunc_normal(0.001),
+ scope='Conv2d_2b_1x1')
+ if spatial_squeeze:
+ aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
+ end_points['AuxLogits'] = aux_logits
+
+ # Final pooling and prediction
+ with tf.variable_scope('Logits'):
+ kernel_size = _reduced_kernel_size_for_small_input(net, [8, 8])
+ net = slim.avg_pool2d(net, kernel_size, padding='VALID',
+ scope='AvgPool_1a_{}x{}'.format(*kernel_size))
+ # 1 x 1 x 2048
+ net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
+ end_points['PreLogits'] = net
+ # 2048
+ logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
+ normalizer_fn=None, scope='Conv2d_1c_1x1')
+ if spatial_squeeze:
+ logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
+ # 1000
+ end_points['Logits'] = logits
+ end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
+ return logits, end_points
+inception_v3.default_image_size = 299
+
+
+def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
+ """Define kernel size which is automatically reduced for small input.
+
+ If the shape of the input images is unknown at graph construction time this
+ function assumes that the input images are is large enough.
+
+ Args:
+ input_tensor: input tensor of size [batch_size, height, width, channels].
+ kernel_size: desired kernel size of length 2: [kernel_height, kernel_width]
+
+ Returns:
+ a tensor with the kernel size.
+
+ TODO(jrru): Make this function work with unknown shapes. Theoretically, this
+ can be done with the code below. Problems are two-fold: (1) If the shape was
+ known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot
+ handle tensors that define the kernel size.
+ shape = tf.shape(input_tensor)
+ return = tf.pack([tf.minimum(shape[1], kernel_size[0]),
+ tf.minimum(shape[2], kernel_size[1])])
+
+ """
+ shape = input_tensor.get_shape().as_list()
+ if shape[1] is None or shape[2] is None:
+ kernel_size_out = kernel_size
+ else:
+ kernel_size_out = [min(shape[1], kernel_size[0]),
+ min(shape[2], kernel_size[1])]
+ return kernel_size_out
+
+
+inception_v3_arg_scope = inception_utils.inception_arg_scope
Code Review / ealt-edge.git / blobdiff